A convenience wrapper for adiv_table()
+ stats_table()
.
Usage
adiv_stats(
biom,
regr = NULL,
stat.by = NULL,
adiv = "Shannon",
split.by = NULL,
transform = "none",
test = "emmeans",
fit = "gam",
at = NULL,
level = 0.95,
alt = "!=",
mu = 0,
p.adj = "fdr"
)
Arguments
- biom
An rbiom object, such as from
as_rbiom()
. Any value accepted byas_rbiom()
can also be given here.- regr
Dataset field with the x-axis (independent; predictive) values. Must be numeric. Default:
NULL
- stat.by
Dataset field with the statistical groups. Must be categorical. Default:
NULL
- adiv
Alpha diversity metric(s) to use. Options are:
"OTUs"
,"Shannon"
,"Chao1"
,"Simpson"
, and/or"InvSimpson"
. Setadiv=".all"
to use all metrics. Default:"Shannon"
Multiple/abbreviated values allowed.- split.by
Dataset field(s) that the data should be split by prior to any calculations. Must be categorical. Default:
NULL
- transform
Transformation to apply. Options are:
c("none", "rank", "log", "log1p", "sqrt", "percent")
."rank"
is useful for correcting for non-normally distributions before applying regression statistics. Default:"none"
- test
Method for computing p-values:
'wilcox'
,'kruskal'
,'emmeans'
, or'emtrends'
. Default:'emmeans'
- fit
How to fit the trendline.
'lm'
,'log'
, or'gam'
. Default:'gam'
- at
Position(s) along the x-axis where the means or slopes should be evaluated. Default:
NULL
, which samples 100 evenly spaced positions and selects the position where the p-value is most significant.- level
The confidence level for calculating a confidence interval. Default:
0.95
- alt
Alternative hypothesis direction. Options are
'!='
(two-sided; not equal tomu
),'<'
(less thanmu
), or'>'
(greater thanmu
). Default:'!='
- mu
Reference value to test against. Default:
0
- p.adj
Method to use for multiple comparisons adjustment of p-values. Run
p.adjust.methods
for a list of available options. Default:"fdr"
Value
A tibble data.frame with fields from the table below. This tibble
object provides the $code
operator to print the R code used to generate
the statistics.
Field | Description |
.mean | Estimated marginal mean. See emmeans::emmeans() . |
.mean.diff | Difference in means. |
.slope | Trendline slope. See emmeans::emtrends() . |
.slope.diff | Difference in slopes. |
.h1 | Alternate hypothesis. |
.p.val | Probability that null hypothesis is correct. |
.adj.p | .p.val after adjusting for multiple comparisons. |
.effect.size | Effect size. See emmeans::eff_size() . |
.lower | Confidence interval lower bound. |
.upper | Confidence interval upper bound. |
.se | Standard error. |
.n | Number of samples. |
.df | Degrees of freedom. |
.stat | Wilcoxon or Kruskal-Wallis rank sum statistic. |
.t.ratio | .mean / .se |
.r.sqr | Percent of variation explained by the model. |
.adj.r | .r.sqr , taking degrees of freedom into account. |
.aic | Akaike Information Criterion (predictive models). |
.bic | Bayesian Information Criterion (descriptive models). |
.loglik | Log-likelihood goodness-of-fit score. |
.fit.p | P-value for observing this fit by chance. |
See also
Other alpha_diversity:
adiv_boxplot()
,
adiv_corrplot()
,
adiv_table()
Other stats_tables:
bdiv_stats()
,
distmat_stats()
,
stats_table()
,
taxa_stats()
Examples
library(rbiom)
biom <- rarefy(hmp50)
adiv_stats(biom, stat.by = "Sex")[,1:6]
#> # Model: gam(.diversity ~ Sex, method = "REML")
#> # A tibble: 1 × 6
#> Sex .mean.diff .h1 .p.val .adj.p .effect.size
#> <chr> <dbl> <fct> <dbl> <dbl> <dbl>
#> 1 Female - Male -0.776 != 0 0.00864 0.00864 -0.804
adiv_stats(biom, stat.by = "Sex", split.by = "Body Site")[,1:6]
#> # Model: gam(.diversity ~ Sex, method = "REML")
#> # A tibble: 5 × 6
#> `Body Site` Sex .mean.diff .h1 .p.val .adj.p
#> <fct> <chr> <dbl> <fct> <dbl> <dbl>
#> 1 Saliva Female - Male -0.262 != 0 0.192 0.357
#> 2 Buccal mucosa Female - Male -0.553 != 0 0.247 0.357
#> 3 Stool Female - Male -0.233 != 0 0.268 0.357
#> 4 Anterior nares Female - Male -0.0729 != 0 0.730 0.730
#> 5 Mid vagina NA NA NA NA NA
adiv_stats(biom, stat.by = "Body Site", test = "kruskal")
#> # Model: kruskal.test(.diversity ~ `Body Site`)
#> # A tibble: 1 × 6
#> .stat .h1 .p.val .adj.p .n .df
#> <dbl> <fct> <dbl> <dbl> <int> <int>
#> 1 38.5 > 0 0.0000000879 0.0000000879 49 4